DataViz Makeover 2

DataViz Makeover 2 for ISSS608: Visual Analytics and Applications.

Fei Jiahui https://www.linkedin.com/in/jiahui-fei-169841109/ (SMU MITB)https://scis.smu.edu.sg/master-it-business
2022-03-13

1 Critique of Original Visualisation

The original visualisation is shown below:

1.1 Clarity

  1. Lack of clear chart title and dashboard title. The original visualisation does not have clear title for the component charts and for the entire dashboard. For example, the title for the charts on the top right corner is called “Weekday Adjacency Matrix”, such a title does not really tell the user what exactly the chart is showing. Only after a careful study, one will then be able to tell the chart is showing the percentage of bus trips to and from particular sub-zones on weekdays. The chart title should be improved so that at one glance, the viewer can tell what the chart is trying to show. Furthermore, a title should be added for the entire dashboard to allow users to understand what analysis this entire dashboard is trying to make.

  2. Row labels and column labels are illegible. Due to the sheer number of sub-zones, the row labels and column labels for the adjacency matrix are difficult, if not impossible, to read. For example, the horizontal axis labels on Destination SZ are totally overlapping with each other, while the labels for origin SZ in the weekend adjacency matrix at the bottom right are missing. Due to such a problem, the clarity of the visualisation is undermined as users are not able to tell which origin sub-zone and which destination sub-zone are being analyzed.

  3. The adjacency matrix is arranging the rows and columns based on alphabetical order, however such an arrangement is not sensible geographically. For example, “Boon Lay Place” is placed next to “Boon Keng” while in fact these two zones are geographically far apart from each other. To analyse the adjacency factor which has a geographical meaning behind it, we should adopt geovisual analysis techniques such as placing the zones into the Singapore map and discover how the bus trips are geographically distributed between different sub-zones so as to enhance clarity of our analysis.

1.2 Aesthetics

  1. Visual tools such as different colours can be used to distinguish trips generated from certain sub-zones with trips attracted to the various sub-zones. Currently, the same colour is used for bar charts denoting trips from and trips to a particular sub-zone, making it difficult to tell the differences. However, if we can use different colours to represent the origin and destination sub-zones respectively, it will be more visually appealing and making it easier to read for potential viewers.

  2. The figure size of the adjacency matrix is too small and views are difficult to hover and select the information they would like to analyze. Due to the very small fig sizes, aesthetics are significantly impaired as viewers can hardly tell the difference in the colours used, and it poses a significant challenge for users to hover and select the data point they want to analyse. For instance, if I would like to know how many per cent of bus trips from Aljunied are bound for Katong on weekdays, it is almost impossible to find and select the right information on this adjacency matrix.

  3. The various worksheets are not properly aligned in the dashboard. It makes the dashboard look messy and less aesthetically appealing when the worksheets are misaligned. For example, the fig length of the weekday adjacency matrix is longer than weekend adjacency matrix and the bottom line of the weekday adjacency matrix is not aligned with the bottom of the weekday time distribution bar charts. Such misalignment will undermine visual aesthetics of the dashboard.

2 Alternative Design

The proposed alternative design is shown below:

2.1 Clarity

  1. Clear chart title and dashboard title will be added to allow users to better undertand the purpose of the particular chart and visualization tool.For example, I will highlight in the chart titles whether it is a time distribution graph for bus trips from a certain origin subzone or it is a destination distribution for bus trips from a certain sub-zone.

  2. I will use geovisual analysis by incorporating all the sub-zones into the Singapore map to visually demonstrate how the bus trips are distributed between various sub-zones. Different from the original design, using a geovisual map will allow users to clearly see the geographical location of all the sub-zones, and have a better understanding of the distribution pattern.

  3. I will also use clear tool tips to indicate important information required by potential users to enhance clarity. For instance, for the geovisual analysis, I will use tool tip to show the destination sub-zone name when hover over the area, different colour steps will also be used to indication the concentration level of the destination distributions.

2.2 Aesthetics

  1. Different colours will be used for time distribution graph of trips to and from a certain sub-zone respectively, so that users will be also to tell the difference at one simple glance. It will also be more visually appealing by using different colours to represent two different attributes.

  2. The dashboard will be properly aligned so as to enhance the visual aesthetics value.

  3. Chart titles will follow the colour scheme of the corresponding bar charts so as to allow users to pick up the important information more easily.

3 Proposed Visualisation

Please refer to the graph below for poposed visualisation. You can also find it on Tableau Public

4 Step By Step Guide

Step Action
1. Open tableau, drag data source files MP14_SUBZONE_WEB_PL.shp and *origin_destination_bus__SZ_202201.csv* into the data source tab, and create a link between the two files. Define the relationship as follows: Subzone N in the shape file equals to the Destination Sz in the csv file
2. Open a new worksheet, name it as “origin”. Convert the attribute Time Per Hour to Dimension
3. Drag time per hour to columns, day type and total trips to rows. Also drag time per hour and origin sz to filters
4. Click time per hour under filters, click edit filter, de-select 0 to 4 as we are only analyzing from 5am onwards
5. Click Origin Sz under filters, click show filter, select single value (list)
6. Right click weekday on the vertical axis, choose rotate label
7. Right click total trips, click format, change font size to 6. Similarly, right click on weekday, click format, change font size to 6. Right click Day type, click format, change font size to 6. Right click hour at the bottom horizontal axis, click format, change font size to 6.
8. Right click total trips, click edit axis, remove axis title
9. Click colour, choose orange
10. Double click on title, change the tile to “Time distribution of bus trips FROM”, click insert, choose origin SZ. Change origin SZ colour to orange and change from to bold, change font size to 8.
11. Right click worksheet origin, choose duplicate, rename it as destination. Remove origin SZ from filters, drag destination SZ to filters. Similarly, click show filter then choose single value (list)
12. Click colour, choose green
13. Double click on title, change the tile to “Time distribution of bus trips TO”, click insert, choose destination SZ. Change destination SZ colour to green and bold.
14. Click to create new worksheet, rename it to map. Drag longitude to columns, latitude to rows. Drag origin Sz to filters. Drag sum(total trips) to colour. Drag geometry, destination SZ and Region N to Detail.
15. Click original SZ under filters, click show filter, then change to single value(list).
16. Click SUM(total trips), quick table calculation, choose percent of total. click edit table calculation, choose table(across)
17. Click colour, click edit colors, change to stepped colour, change opacity to 90%
18. Double click chart title, change to “Destination distribution of all bus trips generated from” click insert, choose origin sz and bold. change font size to 8.
19. Go back to sheet origin, go to the filter, choose apply to worksheets, selected worksheets, and tick map
20. right click on legend and filter, change all font size to 6.
21. Create a new dashboard and drag the worksheet to the positions show in the graph at the right. Save and publish to tableau public.

5 Observations and Insights

5.1 Public bus services are mainly serving short-distance travel needs of commuters.

From the geovisual analysis, we can tell the general destination distribution patterns for bus trips. It is noted that most bus services are serving routes within the same sub-zone or to adjacent sub-zones in the same area. For example, if we choose a random origin sub-zone such as tampines north as shown below, we will see that more than 75% of bus trips are serving destinations within tampines (be it tampines north, east or west.)

As such, we can infer that public bus travel mainly serve as a supplementary service for shorter-distance trips within the same sub-zone or to adjacent sub-zones. For long-distance travel across different regions, alternatives such as MRT could be the main choice for commuters.

5.2 Singapore still has a long way to go to smoothen peak-period travel demand.

It is identified in the Singapore Land Transport Master Plan 2040 that it is a key priority for the authority to encourage pre-peak travel and smoothen Singpore’s peak period travel demand. However, from the illustration below, we can see that there is still a very sharp peak on weekdays around 7am in the morning and 6pm in the afternoon.

Having a very sharp peak-period travel demand will place significant pressure on not only bus services but also the entire land transport system. For example, in the past decade, Singapore’s MRT system was poorly maintained and struggled to cope with the high peak-period demand. One of the worst MRT breakdowns happened during the afternoon peak hours of 7 july 2015, and more than 250,000 commuters were affected. Despite government initiatives such as offering free travel incentives during pre-peak hours, Singapore still has a sharp peak-period travel demand and we still have a long way to go to smoothen the travel demand curve.

5.3 A different CBD during weekends

Singapore’s CBD area is very busy during weekdays but it may see the sharpest drop in traffic and essentially turn into a semi ghost town during weekends. We can see from the chart below where a CBD zone - Cecil experiences a sharp drop in traffic during weekends.

As identified in the Singapore Land Transport Master Plan 2040, the government is trying to bring jobs closer to people’s home by initiating development projects such as Jurong Innovation District and Punggol Digital District. With such new districts being built, the CBD may no longer be overly crowded during weekdays and under-utilised during weekends. As Singapore tries to adopt a more balanced urban development strategy, I believe there will no longer be a single CBD but have multiple small CBDs across the island in the future.